rl is defined as a learning process of how an agent takes actions in an environment to minimize a given loss [1]}. The optimization of the dpd parameters can be viewed through the lens of a rl problem. The dpd acts as an agent that takes actions following a policy, which is optimized to minimize the loss \(\mathcal {L}\) . RL has already been used in the context of communications, e.g., to optimize the constellation mapper and demapper [2]}, [3]}. Here, we consider a different scenario with more components, which raises the problem of how to optimize the DPD parameters \(\theta ^{\text{DPD}}\)  at the sample-based using a loss function \(\mathcal {L}\)  at the symbol-based.
